This paper investigates self-reconfiguration of a modular robotic system, which consists of a cluster of modular vehicles that can attach to each other by a connection mechanism. Thereby, they can form a desired morphology to meet task specific requirements. Reconfiguration can be needed due to limitations from dimensions of passable corridors for an underwater maintenance task, for supplemental instrumentation that is available on a particular robot, or as remedial action if one robot in a cluster suffers from malfunction. Being crucial for autonomous underwater vehicles, energy consumed is employed as a heuristic. The paper shows how the Basic Theta* algorithm can be guided by an energy criterion to calculate a transition from start- to goal morphology. Individual robots are guided while minimizing the overall energy for propulsion and for balancing restoring forces and moments in morphologies. The properties of the proposed self-reconfiguration algorithm are evaluated through simulations and preliminary model tank experiments. The energy based heuristic for reconfiguration is compared to a traditional solution that minimizes the Euclidean distance.

Robotic systems have a high potential for creative learning if they are flexible, accessible and engaging for the user in the experimental process of building and programming robots. In this paper we describe the Fable modular robotic system for creative learning which we develop to enable and motivate anyone to build and program their own robots. The Fable system consists of self-contained modules equipped with sensors and actuators, which users can use to easily assemble a wide range of robots in a matter of seconds. The robots are user-programmable on several levels of abstraction ranging from a simple visual programming language to powerful conventional ones. This paper provides an overview of the design of Fable for different user groups and an evaluation of critical issues when we attempt to integrate the system into an everyday teaching context.

This paper describes a tangible interface, Playte, designed for children animating interactive robots. The system supports physical manipulation of behaviors represented by LEGO bricks and allows the user to record and train their own new behaviors. Our objective is to explore several modes of interaction, i.e. direct remote control, tangible programming, programming by demonstration, and programming by training, to learn the design principles for more accessible, engaging, and playful robots. We evaluate the system experimentally and report on key observations from play sessions. We conclude that Playte facilitates playful activities and is appropriate for the intended target group (age 6+). Further, we discuss lessons learned regarding pros and cons of the different supported interactions modes.

We are developing the Fable modular robotic system as a playware platform that will enable non-expert users to develop robots ranging from advanced robotic toys to robotic solutions to problems encountered in their daily lives. This paper presents the mechanical design of Fable: a chain-based system composed of reconfigurable heterogeneous modules with a reliable and scalable connector. Furthermore, this paper describes tests where the connector design is tested with children, and presents examples of a moving snake and a quadruped robot, as well as an interactive upper humanoid torso.

In this paper we study online gait optimization for modular robots. The learning strategy we apply is distributed, independent on robot morphology, and easy to implement. First we demonstrate how the strategy allows an ATRON robot to adapt to faults and changes in its morphology and we study the strategy's scalability. Second we extend the strategy to learn the parameters of gait-tables for ATRON and M-TRAN robots. We conclude that the presented strategy is effective for online learning of gaits for most types of modular robots and that learning can effectively be distributed by having independent processes learning in parallel.

This paper describes the electronics and communication system of Odin, a novel heterogeneous modular robot made of links and joints. The electronics is divided into two printed circuit boards: a General board with reusable components and a Specific board with non-reusable components. While the General board is common to the design of every type of module, such as power, actuator, sensor and structure, the Specific board is unique to each type of module. The communication system, one of the most important reusable components of Odin, is based on local buses that can be extended by bridging electrical signals.

This paper presents a novel biologically- inspired hierarchical approach to organizing and controlling modular robots. The purpose of our approach is to decompose the complexity of assembling and commanding a functional robot made of numerous simple modules (thousands to millions) by introducing a hierarchy of structure and control. The robots we describe incorporate anatomically-inspired parts such as muscles, bones and joints, and these parts in turn are assembled from modules. Each of those parts encapsulates one or more functions, e.g. a muscle can contract. Control of the robot can then be cast as a problem of controlling its anatomical parts rather than each discrete module. We show simulation results from experiments using gradient-based primitives to control parts of increasingly complex robots, including snake, crawler, cilia-surface, arm- joint-muscle and grasping robots. We conclude that this approach is promising for future many-modules systems, but is currently impractical on most existing platforms.

Generic simulation platforms such as player/stage are an essential tool in mobile robotics, but until now no similar platforms have been available for the field of self-reconfigurable robots. We here present a generic simulation platform for modular, self-reconfigurable robots: the unified simulator for self-reconfigurable robots (USSR). USSR is based on a physics engine, allowing simulation of both self-reconfiguration and dynamic interaction with the environment. The simulator is implemented as a framework that provides numerous components that can be combined to form new or existing modular robots, allowing easy experimentation: USSR currently includes support for the ATRON, Odin, and M-TRAN modular robots.

Scaling down the module size of a self-reconfigurable robot will have a profound effect on the module's characteristics, e.g. strength to mass ratio. In this paper we explore how the characteristics of chains of modules, specifically locomotion velocity and best gait type, might change with the scale of those modules. The simulated experiments we report on here examine module sizes from (11 mum to 698 mum radius) and chain lengths from 3 to 30 modules. All gaits tested were based on central pattern generators optimized using a genetic algorithm and hill climbing. Our results show that scaling affects both the preferred type of gait as well as a chain's overall performance (average velocity). In summary, there is a tradeoff where larger scales face the challenge of overcoming gravity, while smaller sizes face the challenge of staying in contact with the ground and the friction it provides. We show that in between these two extremes lies a "best" module size for given environmental, physical, and engineering constraints

This paper presents a series of experiments on fault tolerant self-reconfiguration of the ATRON robotic system. For self-reconfiguration we use a previously described distributed control strategy based on meta-modules that emerge, move and stop. We perform experiments on three different types of failures: 1) Action failure: On the physical platform we demonstrate how roll-back of actions are used to achieve tolerance to collision with obstacles and other meta-modules. 2) Module failure: In simulation we show, for a 500 module robot, how different degrees of catastrophic module failure affect the robot's ability to shape-change to support an insecure roof. 3) Robot failure: In simulation we demonstrate how robot faults such as a broken robot bone can be emergent self-repaired by exploiting the redundancy of self-reconfigurable modules. We conclude that the use of emergent, distributed control, action roll-back, module redundancy, and self-reconfiguration can be used to achieve fault tolerant, self-repairing robots

In this paper we present a 2D meta-module for the ATRON robot, which simplifies the motion constraints significantly. The motion capabilities of the new meta-module is similar to that of previous sliding cube style modules with the addition of one extra action, which is shown to improve the motion capabilities of the modules greatly. In general our work shows that if three simple actions, which is implemented by our proposed meta-module, is implemented by a self-reconfigurable robot module or meta-module then the control of the robot will be simple. The improved motion capabilities allow us to use offline planning for large groups consisting of hundreds of meta- modules. The simple motion constraints of the meta-module further allows us to implement a distributed cluster flow locomotion gait, with the interesting property that the group of meta-modules self-organizes into a shape that makes the locomotion efficient.

The ATRON self-reconfigurable robot consists of simple interconnected modules. Modules move relative to other modules and as a result change the shape of the robot. The ATRON modules are difficult to control because of complex motion constraints on the modules. Motion constraints are reduced by using meta-modules composed of three modules. A meta-module may emerge from unstructured groups of modules if three modules are connected in the right configuration. The meta-module then moves on a surface of modules and stop at another position. To attract moving meta-modules and thereby to specify the shape-changing task of the robot we use attraction-points. In this work we evolve a distributed artificial neural network controller for the modules. The controller is identical on every module and controls when a meta-module emerges, how it move and when it stops. In simulation we demonstrate how this control strategy allows the ATRON robot to shape-change to support an unstable roof, build a bridge across a gap and to self-repair a broken bone. We conclude that the control strategy is able to shape-change and self-repair the ATRON robot independent on whether it consists of dozens, hundreds or thousands of modules

The ATRON self-reconfigurable robot consists of simple one degree of freedom ATRON modules. The motion capabilities of an individual module are therefore quite limited. To compensate for this, meta-modules composed of more than one module are used to shape-change the system. Meta-modules emerge from the environment created by other modules, move on the surface of other modules and stop at a new position. The flow of meta-modules, from one place to another on the structure of modules, realizes the desired self-reconfiguration. In this paper we compare six different meta-module types composed of ATRON modules. Variations of meta-module morphology and meta-actions are investigated for its ability to shape-change the robot. We conclude that two of the investigated meta-module types are able to shape-change the robot to an acceptable extent